Method and system for identifying abnormal available capacity of electrochemical energy storage system

Abstract

The invention provides an electrochemical energy storage system available capacity abnormity identification method and system. The method comprises the following steps: obtaining operation data of an energy storage battery cabin in a deep charge-discharge cycle and carrying out preprocessing; on the basis of the preprocessed operation data, whether an abnormal attenuation suspected battery meeting a preset charging high-discharging low-voltage deviation criterion exists in the energy storage battery cabin or not is recognized; calculating a first capacity loss rate of the abnormal attenuation suspicion battery by adopting an ampere-hour integral method, calculating a second capacity loss rate of the abnormal attenuation suspicion battery based on a mapping relation between the open-circuit voltage and the state of charge, and further determining a final capacity loss rate of the abnormal attenuation suspicion battery; and in response to the situation that the abnormal attenuation suspicion battery is not identified or the final capacity loss rate is lower than a first threshold value, calculating the charge state range of all batteries based on the battery voltage data of the energy storage battery cabin in the standing state, and determining the available capacity loss rate, the problem that an electrochemical energy storage system is difficult to accurately reflect capacity information of a battery compartment is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrochemical energy storage systems, and particularly relates to an electrochemical energy storage system available capacity anomaly identification method and system. BACKGROUND

[0002] In an electrochemical energy storage system, the available capacity of a battery cabin is a core performance indicator, directly determining the actual energy storage and release capacity of the system, and playing a key role in ensuring the reliability of energy supply, the economy of system operation, and the service life. In actual operation, the available capacity is often significantly lower than the theoretical value due to the consistency deviation of the battery pack and the influence of factors such as temperature and aging, thereby multiple constraints on the overall performance and investment return of the system. Therefore, accurate and efficient estimation of the available capacity of the energy storage system is needed, and reasonable operation and maintenance measures are taken to delay capacity degradation, thereby ensuring long-term efficient and stable operation.

[0003] In related technologies, existing solutions are committed to evaluating the system health state through cell-level capacity prediction. For example, by using a two-stage voltage prediction model, combining charging and discharging working condition data, the maximum available capacity of the cell is predicted, and the health state is calculated accordingly. The core of this solution is to predict the charging and discharging processes to the cut-off voltage respectively, calculate the difference between the charging and discharging capacities under the two predictions, and combine the initial capacity to evaluate the battery health state.

[0004] However, such methods still have certain limitations in actual deployment. The effect depends on the accuracy of the preset model, two voltage prediction steps need to be performed, the completeness and quality of the input data are sensitive, the calculation complexity is high, and the engineering application difficulty is increased. In addition, this type of method is usually based on standard working condition assumptions, which is different from the actual operating conditions, and it is difficult to achieve accurate and robust capacity anomaly identification and positioning in industrial scenarios with low sampling frequency (such as ≥1 second / second), especially it cannot effectively distinguish the influence degree of battery intrinsic degradation and consistency deviation on the available capacity of the system, which restricts the accuracy and timeliness of operation and maintenance decisions. SUMMARY

[0005] The present application provides an electrochemical energy storage system available capacity anomaly identification method and system to solve the problem that the electrochemical energy storage system cannot accurately reflect the available capacity information of the battery cabin.

[0006] The first aspect of the present application provides an electrochemical energy storage system available capacity anomaly identification method, which comprises: obtaining running data of an energy storage battery cabin in at least one complete deep charge-discharge cycle, the running data comprising time series of battery voltage and current data; The operating data is preprocessed to remove invalid data and ensure that the deep charge-discharge cycle meets the preset integrity conditions; Based on the preprocessed operating data, identify whether there are any suspected abnormally degraded batteries in the energy storage battery compartment that meet the preset high charge and low discharge voltage deviation criteria. In response to the identification of the suspected abnormally degraded battery, the first capacity loss rate of the suspected abnormally degraded battery is calculated using the ampere-hour integration method, and the second capacity loss rate of the suspected abnormally degraded battery is calculated based on the mapping relationship between open circuit voltage and state of charge. Then, the final capacity loss rate of the suspected abnormally degraded battery is determined based on the second capacity loss rate. In addition, in response to the failure to identify the suspected abnormal degradation battery or the final capacity loss rate being lower than a first threshold, the state of charge range of all batteries is calculated based on the battery voltage data of the energy storage battery compartment in a static state, and the state of charge range is determined as the available capacity loss rate caused by abnormal battery consistency.

[0007] By integrating the ampere-hour integral method with the open-circuit voltage-state-of-charge mapping relationship, a two-factor capacity loss calculation is performed on suspected abnormally degraded batteries. The capacity loss caused by consistency anomalies is directly quantified by utilizing the state-of-charge range. This decouples and quantifies the causes of abnormal available capacity in the battery compartment during a single deep charge-discharge cycle, which helps alleviate the problems of inaccurate capacity information and difficulty in locating the problem in existing technologies, and improves the accuracy and efficiency of energy storage system operation and maintenance.

[0008] Optionally, the preprocessing step for the runtime data includes: Remove data points in the operating data whose battery voltage values ​​exceed the preset safe voltage range; The null values ​​in the running data are filled in; Determine whether the deep charge-discharge cycle meets the preset cycle conditions; the preset cycle conditions include: the maximum voltage at the charging end is greater than the first voltage threshold, the minimum voltage at the discharging end is less than the second voltage threshold, and the resting time after the discharge is completed is greater than the preset time.

[0009] By performing preprocessing steps such as removing abnormal voltage values, filling in missing values, and verifying cycle integrity on the operating data, the quality and reliability of the data used for subsequent capacity anomaly identification can be improved, providing support for accurately judging battery degradation and consistency anomalies.

[0010] Optionally, after determining whether the deep charge-discharge cycle meets the preset cycle conditions, the method further includes: Calculate the total ampere-hour integral charge of the deep charge-discharge cycle; Determine whether the ratio of the total ampere-hour integrated capacity to the rated capacity of the energy storage battery compartment is within a preset reasonable range; If the ratio exceeds the preset reasonable range, the data for this loop is determined to be invalid and discarded.

[0011] By further comparing the ratio of the total ampere-hour integrated power to the rated capacity after verifying the integrity of the cycle, it is possible to identify unreliable data caused by current sensor drift or severe data loss, thereby reducing the risk of errors in the subsequent capacity anomaly identification process due to unreliable data.

[0012] Optionally, the step of identifying whether there are any suspected abnormally degraded batteries in the energy storage battery compartment that meet a preset high-charge-low-discharge voltage deviation criterion includes: For a battery cluster in the energy storage battery compartment, calculate the median of all battery voltages at the end of charging and the median of all battery voltages at the end of discharging. Calculate a first difference between the voltage of each battery at the end of charging and the median voltage at the end of charging, and a second difference between the voltage of each battery at the end of discharging and the median voltage at the end of discharging; Batteries that simultaneously satisfy the condition that the first difference is greater than the positive deviation threshold and the second difference is less than the negative deviation threshold are marked as suspected abnormal degradation batteries.

[0013] By employing a collaborative criterion combining positive deviation at the charging end and negative deviation at the discharging end to identify suspected batteries with abnormal degradation, it helps to improve the accuracy of locating voltage anomalies caused by battery capacity degradation and reduces misjudgments caused by connection impedance or instantaneous measurement noise.

[0014] Optionally, the formula for calculating the first capacity loss rate of the suspected abnormally degraded battery using the ampere-hour integration method is as follows: ; in, This is the first capacity loss rate; The actual charge / discharge amount is obtained by integrating the current over time during a deep charge / discharge cycle. This refers to the battery's nominal capacity.

[0015] The capacity loss rate is calculated by using the ampere-hour integration method, and the actual charge and discharge amounts are compared with the nominal capacity of the battery, providing a direct calculation method based on operating data for quantifying the capacity decay of a single battery cell.

[0016] Optionally, the step of calculating the second capacity loss rate of the suspected abnormally degraded battery based on the mapping relationship between open-circuit voltage and state of charge includes: Obtain the interpolated open-circuit voltage-state-of-charge curve; Based on the voltage of the suspected abnormally degraded battery in a static state, its first state of charge value is determined using the open-circuit voltage-state-of-charge curve. Calculate the average state of charge of all normal batteries in the battery cluster, excluding the suspected abnormally degraded battery; The second capacity loss rate is calculated based on the first state of charge value and the average state of charge value.

[0017] By using the open-circuit voltage-state-of-charge curve to obtain the state of charge of the suspect battery and comparing it with the average state of charge of the normal batteries in the cluster to calculate the capacity loss rate, an alternative calculation path that does not rely on current integration is provided. This helps to compensate for the cumulative error that may exist in the ampere-hour integration method in low sampling frequency scenarios, thereby improving the reliability of the capacity loss assessment results.

[0018] Optionally, the step of determining the final capacity loss rate of the suspected abnormally degraded battery based on the second capacity loss rate is to calculate the arithmetic mean of the first capacity loss rate and the second capacity loss rate.

[0019] By arithmetically averaging the two capacity loss rates obtained from the ampere-hour integration method and the open-circuit voltage-state-of-charge mapping method, the calculation results from two different principles—dynamic current integration and static voltage characteristics—are integrated. This helps to smooth out the calculation bias of a single method, thereby improving the robustness of the final capacity loss rate assessment results.

[0020] Optionally, the step of calculating the state-of-charge range of all batteries based on the battery voltage data of the energy storage battery compartment in a static state includes: Select the battery voltage data at the moment when the deep charge-discharge cycle ends and the battery has been left to stand for a preset stable time. Using the open-circuit voltage-state-of-charge curve, the voltage of each battery is mapped to the corresponding state-of-charge value; Find the maximum and minimum values ​​of the state of charge (SOC) values ​​of all batteries, and calculate the difference between them as the SOC range.

[0021] By utilizing the voltage data after static stabilization and combining it with the open-circuit voltage-state-of-charge curve, the battery voltage is mapped to a state-of-charge value, and its range is calculated. This provides a direct measurement method based on the internal state of the battery to quantify the impact of differences in consistency between batteries on available capacity.

[0022] Optionally, after determining the final capacity loss rate or the available capacity loss rate, the method further includes: If the final capacity loss rate exceeds the second threshold, an operation and maintenance instruction is generated to recommend replacing the battery suspected of abnormal degradation. If the available capacity loss rate is lower than the third threshold, an operation and maintenance instruction is generated to enable the battery management system balancing function and continuously monitor it. If the available capacity loss rate exceeds the third threshold, an operation and maintenance instruction is generated to perform recharging or discharging operations on the battery with abnormal state of charge.

[0023] By generating differentiated operation and maintenance instructions based on the quantified capacity loss rate, the diagnostic results can be associated with specific operation and maintenance operations, providing targeted operational guidance for on-site maintenance personnel to handle abnormal battery degradation or inconsistency issues.

[0024] The second aspect of this application provides a system for identifying anomalies in the available capacity of an electrochemical energy storage system. The system includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the method for identifying anomalies in the available capacity of an electrochemical energy storage system as described in the first aspect.

[0025] The electrochemical energy storage system can use a capacity anomaly identification system to execute a stored computer program through a processor, thereby enabling the capacity anomaly identification method described in the first aspect to be implemented, providing hardware support for the automated operation and large-scale application of the above method.

[0026] As can be seen from the above technical solutions, this application provides a method and system for identifying anomalies in the usable capacity of an electrochemical energy storage system. This involves acquiring operational data of the energy storage battery compartment during at least one complete deep charge-discharge cycle, including time-series battery voltage and current data; preprocessing the operational data to remove invalid data and ensure that the deep charge-discharge cycle meets preset integrity conditions; identifying, based on the preprocessed operational data, whether there are any suspected abnormally degraded batteries in the energy storage battery compartment that meet preset high-charge-low-discharge voltage deviation criteria; and, in response to identifying the suspected abnormally degraded batteries, calculating the abnormal degradation using the ampere-hour integration method. The system calculates the first capacity loss rate of the suspected battery and the second capacity loss rate of the suspected abnormally degraded battery based on the mapping relationship between open-circuit voltage and state of charge. Then, it determines the final capacity loss rate of the suspected abnormally degraded battery based on the second capacity loss rate. In response to the failure to identify the suspected abnormally degraded battery or the final capacity loss rate being lower than a first threshold, it calculates the state of charge range of all batteries based on the battery voltage data of the energy storage battery compartment in a static state, and determines the state of charge range as the available capacity loss rate caused by abnormal battery consistency, so as to solve the problem that electrochemical energy storage systems are difficult to accurately reflect the available capacity information of the battery compartment. Attached Figure Description

[0027] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating the method for identifying anomalies in the available capacity of an electrochemical energy storage system provided in an embodiment of this application. Detailed Implementation

[0029] The embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described below do not represent all embodiments consistent with this application. They are merely examples of systems and methods consistent with some aspects of this application.

[0030] To address the issue of electrochemical energy storage systems failing to accurately reflect the capacity information of the battery compartment, see [reference needed]. Figure 1 This application provides a method for identifying anomalies in the available capacity of an electrochemical energy storage system, the method comprising: S100: Acquire operational data of the energy storage battery compartment during at least one complete deep charge-discharge cycle.

[0031] It should be understood that, in the process of acquiring operational data for the energy storage battery compartment, at least one complete deep charge-discharge cycle is defined as a complete process in which the energy storage battery compartment starts from a fully charged state, undergoes deep discharge until it reaches a preset minimum charge level, and then undergoes deep charging until it is fully charged again. The acquisition frequency can be 5 seconds. The operational data includes time-series battery voltage and current data. This time-series data not only includes the specific values ​​of battery voltage and current at each moment during the deep charge-discharge cycle but also records the trends of these values ​​over time.

[0032] S200: Preprocesses the operating data to remove invalid data and ensure that the deep charge-discharge cycle meets the preset integrity conditions.

[0033] In some embodiments, the step of preprocessing runtime data includes: Remove data points in the operating data where the battery voltage value exceeds the preset safe voltage range.

[0034] It should be understood that the preset safe voltage range is selectable [2.7V-3.65V]. Any data point corresponding to a battery voltage value outside this range will be considered invalid and discarded to ensure the validity and safety of the data.

[0035] Fill in the null values ​​in the running data.

[0036] It should be understood that for null values ​​in the running data, a linear filling method is used to process them. That is, based on the values ​​of the data points before and after the null value, the value to be filled for the null value is calculated by linear interpolation, thereby ensuring the continuity and integrity of the data.

[0037] Determine whether the deep charge-discharge cycle meets the preset cycle conditions; the preset cycle conditions include: the maximum voltage at the end of charging is greater than the first voltage threshold, the minimum voltage at the end of discharging is less than the second voltage threshold, and the resting time after the discharge is completed is greater than the preset time.

[0038] It should be understood that when determining whether a deep charge-discharge cycle meets the preset cycle conditions, three conditions must be met simultaneously: the maximum voltage at the end of charging must be greater than a preset first voltage threshold to ensure the battery reaches sufficient charge during charging; the minimum voltage at the end of discharging must be less than a preset second voltage threshold to ensure the battery can fully release its charge during discharging; and the resting time after discharging must be greater than a preset time to ensure the battery can recover to a stable state during resting, thereby accurately assessing the battery's usable capacity. The first voltage threshold can be selected as 3.55V; the second voltage threshold can be selected as 2.9V; and the preset time can be selected as 2 hours.

[0039] By performing preprocessing steps such as removing abnormal voltage values, filling in missing values, and verifying cycle integrity on the operating data, the quality and reliability of the data used for subsequent capacity anomaly identification can be improved, providing support for accurately judging battery degradation and consistency anomalies.

[0040] S300: Based on preprocessed operating data, identify whether there are any suspected abnormally degraded batteries in the energy storage battery compartment that meet the preset high charge and low discharge voltage deviation criteria.

[0041] In some embodiments, the step of identifying whether there are abnormally degraded suspected batteries in the energy storage battery compartment that meet a preset high-charge-low-discharge voltage deviation criterion includes: For a battery cluster in the energy storage battery compartment, calculate the median of all battery voltages at the end of charging and the median of all battery voltages at the end of discharging.

[0042] Calculate the first difference between the voltage of each battery at the end of charging and the median voltage at the end of charging, and the second difference between the voltage of each battery at the end of discharging and the median voltage at the end of discharging.

[0043] Batteries that simultaneously meet the conditions of a first difference greater than a positive deviation threshold and a second difference less than a negative deviation threshold are marked as suspected abnormal degradation batteries.

[0044] It should be understood that the positive deviation threshold can be 60mV; the negative deviation threshold can be 90mV. The formula for calculating the median voltage of all batteries at the end of charging / discharging is: V med，chg =median(V chg，1 V chg，2 , ..., V chg，N ).

[0045] Among them, V med，chg Represents the median voltage of all batteries at the charging / discharging end. This value is obtained by taking the voltage value V of each battery at the charging / discharging end. chg，1 V chg，2 up to V chg，N It was obtained by calculating the median.

[0046] By employing a collaborative criterion combining positive deviation at the charging end and negative deviation at the discharging end to identify suspected batteries with abnormal degradation, it helps to improve the accuracy of locating voltage anomalies caused by battery capacity degradation and reduces misjudgments caused by connection impedance or instantaneous measurement noise.

[0047] S400: In response to the identification of a suspected battery with abnormal degradation, the first capacity loss rate of the suspected battery with abnormal degradation is calculated using the ampere-hour integration method; and the second capacity loss rate of the suspected battery with abnormal degradation is calculated based on the mapping relationship between open circuit voltage and state of charge, and then the final capacity loss rate of the suspected battery with abnormal degradation is determined based on the second capacity loss rate.

[0048] Specifically, the ampere-hour integration method is first used to calculate the actual charge and discharge amounts of the suspected abnormally degraded battery during charge and discharge cycles. The calculation formula is as follows: .

[0049] in, The current represents the real-time current, measured in amperes (A), and t1 to t2 represent the complete charge-discharge cycle.

[0050] Through the Integrating over the time interval from t1 to t2 yields the actual charge and discharge amounts during this period. The unit is ampere-hour (Ah).

[0051] Next, in order to assess the battery's capacity loss, the actual charge and discharge amounts were... With the battery's nominal capacity Comparison of nominal battery capacity Capacity loss is the amount of charge a battery can store or release under standard conditions, and it is an important performance indicator of a battery. The capacity loss rate is obtained by calculating the ratio of the actual charge / discharge amount to the nominal capacity. .

[0052] In some embodiments, the formula for calculating the first capacity loss rate of a suspected battery with abnormal degradation using the ampere-hour integration method is as follows: .

[0053] in, The actual charge / discharge capacity is expressed in ampere-hours (Ah). This refers to the battery's nominal capacity, measured in ampere-hours (Ah).

[0054] Finally, the calculated first capacity loss rate Compare with the industry-standard threshold (5%). If A value greater than 5% indicates significant abnormal degradation of the battery.

[0055] In some embodiments, the step of calculating the second capacity loss rate of the suspected abnormally degraded battery based on the mapping relationship between open-circuit voltage and state of charge includes: Obtain the interpolated open-circuit voltage-state-of-charge curve.

[0056] It should be understood that obtaining the interpolated open-circuit voltage-state-of-charge curve is to more accurately reflect the changes in open-circuit voltage of the battery under different states of charge. Since actual measurements may not cover all state of charge points, interpolation can produce a more continuous and smoother curve, thereby improving the accuracy of subsequent calculations of the second capacity loss rate.

[0057] Specifically, since the sampling frequency used for analysis and calculation in electrochemical energy storage systems is often greater than 1 second / time, it is necessary to estimate the current state of capacity of the remaining batteries using the open-circuit voltage method, thereby comprehensively judging the capacity loss. The open-circuit voltage-state-of-charge (SOC-OCV) curve for lithium-ion batteries is measured with an interval of 5% SOC. The linear interpolation formula for open-circuit voltage-state-of-charge is as follows: .

[0058] Among them, V k and V k+1 These represent the open-circuit voltage values ​​corresponding to specific SOC (State of Charge) points k and k+1 of a lithium-ion battery, respectively; SOC represents the battery's state of charge, i.e., the ratio of the battery's remaining capacity to its full capacity. k-1 SOC k SOC k+1 These represent three different state-of-charge points selected when measuring the SOC-OCV curve, where SOC... k-1 Less than SOC k SOC k Less than SOC k+1These three points are used to construct and interpolate the SOC-OCV curve to more accurately estimate the battery state.

[0059] Based on the voltage of the suspected abnormally degraded battery in a static state, its first state of charge value is determined using the open-circuit voltage-state-of-charge curve.

[0060] Specifically, first, obtain the voltage value of the suspected abnormally degraded battery in a static state. Then, compare this voltage value with the SOC-OCV curve. On the SOC-OCV curve, find the point corresponding to this voltage value; the state of charge value corresponding to this point is the first state of charge value.

[0061] Calculate the average state of charge of all normal cells in the battery cluster, excluding those suspected of abnormal degradation.

[0062] Specifically, the formula for calculating the average state of charge is: .

[0063] Where N represents the number of batteries in the battery cluster; SOC i This represents the state of charge (SOC) value of the i-th battery.

[0064] The second capacity loss rate is calculated based on the first state of charge value and the average state of charge.

[0065] In some embodiments, the step of determining the final capacity loss rate of a suspected abnormally degraded battery based on the second capacity loss rate is: calculating the arithmetic mean of the first capacity loss rate and the second capacity loss rate.

[0066] By arithmetically averaging the two capacity loss rates obtained from the ampere-hour integration method and the open-circuit voltage-state-of-charge mapping method, the calculation results from two different principles—dynamic current integration and static voltage characteristics—are integrated. This helps to smooth out the calculation bias of a single method, thereby improving the robustness of the final capacity loss rate assessment results.

[0067] Finally, the average SOC of all cells in the battery cluster is taken as the final capacity loss rate of the suspected abnormal degradation battery, and its formula can be: .

[0068] in, This indicates the final capacity loss rate of batteries suspected of abnormal degradation. This represents a function that takes a reference value or a specially processed value from the average SOC of all batteries in the battery cluster and a second capacity loss rate (calculated based on the first state of charge value and the average state of charge value) to determine the final capacity loss rate.

[0069] By using the open-circuit voltage-state-of-charge curve to obtain the state of charge of the suspect battery and comparing it with the average state of charge of the normal batteries in the cluster to calculate the capacity loss rate, an alternative calculation path that does not rely on current integration is provided. This helps to compensate for the cumulative error that may exist in the ampere-hour integration method in low sampling frequency scenarios, thereby improving the reliability of the capacity loss assessment results.

[0070] S500: In response to the failure to identify any suspected abnormal degradation batteries or the final capacity loss rate being lower than a first threshold, the state of charge range of all batteries is calculated based on the battery voltage data of the energy storage battery compartment in a static state, and the state of charge range is determined as the usable capacity loss rate caused by abnormal battery consistency.

[0071] In some embodiments, the step of calculating the state-of-charge range of all batteries based on the battery voltage data of the energy storage battery compartment in a static state includes: Select battery voltage data at the moment when the battery has been left to stand for a preset stable time after the deep charge-discharge cycle has ended.

[0072] By using the open-circuit voltage-state-of-charge curve, the voltage of each battery is mapped to the corresponding state-of-charge value.

[0073] Find the maximum and minimum values ​​of the state of charge (SOC) values ​​for all batteries, and calculate the difference between them as the SOC range.

[0074] It should be understood that if no suspected abnormal degradation batteries are identified, or if the calculated final capacity loss rate is lower than a pre-set first threshold, it indicates that the current battery degradation is not significant or has not reached a level requiring special attention. At this point, in order to further assess the battery consistency and the potential loss of usable capacity, the system will switch to in-depth analysis based on the battery voltage data of the energy storage battery compartment in a static state.

[0075] The system collects voltage data from all batteries in a resting state, reflecting their static characteristics when there is no charging or discharging activity. Next, the system calculates the state-of-charge (SOC) range for these batteries, which is the difference between the maximum and minimum SOC values ​​across all batteries. This range directly reflects the consistency of SOC among the batteries; a larger range indicates greater SOC differences and poorer consistency.

[0076] Specifically, data is selected at a certain moment after deep discharge and 2 hours of rest, and the voltage data of all individual cells at that moment are collected. The difference between the maximum and minimum SOC values ​​is calculated according to the battery cluster cycle. If it exceeds 100mV, the usable capacity loss is calculated.

[0077] Finally, the system identifies this state-of-charge (SOC) range as the usable capacity loss rate caused by abnormal battery consistency. This is because when there is poor consistency between batteries, some batteries may reach the charge / discharge cutoff condition prematurely during charging and discharging, thus limiting the overall depth of charge and discharge of the battery pack and resulting in a loss of usable capacity. Therefore, by calculating the SOC range, the usable capacity loss rate caused by abnormal battery consistency can be indirectly assessed.

[0078] Specifically, the formula for calculating SOC estimation based on the interpolated SOC-OCV curve is as follows: .

[0079] Among them, SOC i This indicates the state of charge of the i-th battery cell, where OCV represents the open-circuit voltage, V. i This represents the voltage value of the i-th battery cell at a specific moment (e.g., a moment after 2 hours of rest following deep discharge). By utilizing the pre-acquired or interpolated SOC-OCV curve, the voltage value of the battery cell is converted into the corresponding state of charge value, thereby achieving an accurate estimation of the state of charge of each battery cell.

[0080] The formula for calculating the available capacity loss rate is: .

[0081] in, This represents the maximum state of charge (SOC) across all individual battery cells. This represents the minimum state of charge among all battery cells.

[0082] This formula quantifies the inconsistency in the state of charge (SOC) among individual cells within a battery cluster by calculating the difference between the maximum and minimum SOC. This inconsistency is often closely related to the loss of usable battery capacity. Therefore, the difference between the maximum and minimum SOC can serve as an important indicator for assessing the final usable capacity loss of a battery cluster due to abnormal SOC consistency.

[0083] In some embodiments, after determining whether a deep charge-discharge cycle meets a preset cycle condition, the method further includes: Calculate the total ampere-hour integral charge during a deep charge-discharge cycle.

[0084] Determine whether the ratio of the total ampere-hour integrated capacity to the rated capacity of the energy storage battery compartment is within a preset reasonable range.

[0085] If the ratio exceeds the preset reasonable range, the data for this loop is deemed invalid and discarded.

[0086] It should be understood that the total ampere-hour integrated capacity is obtained by integrating the current over time during a deep charge-discharge cycle. It reflects the actual amount of electricity charged or discharged by the battery during that cycle. The preset reasonable range is an interval set based on the rated capacity of the energy storage battery compartment, actual usage conditions, and experience. This range ensures that the collected cycle data is valid and representative. When the ratio of the total ampere-hour integrated capacity to the rated capacity of the energy storage battery compartment exceeds this preset reasonable range, it indicates that an abnormality may have occurred during this deep charge-discharge cycle, such as excessive measurement error or unstable battery operation. This results in the data not accurately reflecting the battery's true performance, so the data for this cycle is deemed invalid and discarded to ensure the accuracy and reliability of subsequent identification of abnormal battery usable capacity.

[0087] In some embodiments, after determining the final capacity loss rate or available capacity loss rate, the method further includes: If the final capacity loss rate exceeds the second threshold, an operation and maintenance instruction will be generated to recommend replacing the battery suspected of abnormal degradation.

[0088] If the available capacity loss rate is lower than the third threshold, an operation and maintenance instruction is generated to enable the battery management system balancing function and continuously monitor it.

[0089] If the available capacity loss rate exceeds the third threshold, an operation and maintenance instruction is generated to perform recharging or discharging operations on the battery with abnormal state of charge.

[0090] Specifically, the second threshold can be set to 20% of the battery's initial rated capacity. This setting is based on the fact that when the battery's capacity decays to this percentage during its normal usage cycle, its performance has significantly decreased, and continued use may pose safety hazards and reduce economic efficiency. Therefore, it is recommended to replace the battery to ensure stable system operation. The third threshold can be set to 5% of the battery's initial rated capacity. When the available capacity loss rate is lower than this value, it indicates that the imbalance between batteries is still within a controllable range. This can be effectively improved by enabling the battery management system's balancing function. If the threshold is exceeded, batteries with abnormal state of charge must be immediately recharged or discharged to prevent further deterioration of battery performance.

[0091] By generating differentiated operation and maintenance instructions based on the quantified capacity loss rate, the diagnostic results can be associated with specific operation and maintenance operations, providing targeted operational guidance for on-site maintenance personnel to handle abnormal battery degradation or inconsistency issues.

[0092] This application also provides a system for identifying anomalies in the available capacity of an electrochemical energy storage system in some embodiments. The system includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the method for identifying anomalies in the available capacity of an electrochemical energy storage system described in the above embodiments.

[0093] The capacity anomaly identification system of the electrochemical energy storage system can implement the capacity anomaly identification method in the first aspect by executing the stored computer program through the processor, thus providing hardware support for the automated operation and large-scale application of the above method.

[0094] As can be seen from the above technical solutions, the embodiments of this application provide a method and system for identifying anomalies in the usable capacity of an electrochemical energy storage system. This involves acquiring operational data of the energy storage battery compartment during at least one complete deep charge-discharge cycle, including time-series battery voltage and current data; preprocessing the operational data to remove invalid data and ensure that the deep charge-discharge cycle meets preset integrity conditions; identifying, based on the preprocessed operational data, whether there are any suspected abnormally degraded batteries in the energy storage battery compartment that meet preset high-charge-low-discharge voltage deviation criteria; and, in response to the identification of suspected abnormally degraded batteries, calculating the anomaly using the ampere-hour integration method. The system calculates the first capacity loss rate of suspected degraded batteries and the second capacity loss rate of suspected abnormally degraded batteries based on the mapping relationship between open-circuit voltage and state of charge. Then, it determines the final capacity loss rate of suspected abnormally degraded batteries based on the second capacity loss rate. In response to the failure to identify suspected abnormally degraded batteries or the final capacity loss rate being lower than a first threshold, it calculates the state of charge range of all batteries based on the battery voltage data of the energy storage battery compartment in a static state, and determines the state of charge range as the available capacity loss rate caused by abnormal battery consistency, so as to solve the problem that electrochemical energy storage systems are difficult to accurately reflect the available capacity information of the battery compartment.

[0095] Similar parts between the embodiments provided in this application can be referred to mutually. The specific implementation methods provided above are only a few examples under the overall concept of this application and do not constitute a limitation on the scope of protection of this application. For those skilled in the art, any other implementation methods extended from the solution of this application without creative effort shall fall within the scope of protection of this application.

Claims

1. A method for identifying available capacity anomalies in an electrochemical energy storage system, characterized in that, The method includes: Acquire operational data of the energy storage battery compartment during at least one complete deep charge-discharge cycle, the operational data including time-series battery voltage and current data; The operating data is preprocessed to remove invalid data and ensure that the deep charge-discharge cycle meets the preset integrity conditions; Based on the preprocessed operating data, identify whether there are any suspected abnormally degraded batteries in the energy storage battery compartment that meet the preset high charge and low discharge voltage deviation criteria. In response to the identification of the suspected abnormally degraded battery, the first capacity loss rate of the suspected abnormally degraded battery is calculated using the ampere-hour integration method, and the second capacity loss rate of the suspected abnormally degraded battery is calculated based on the mapping relationship between open circuit voltage and state of charge. Then, the final capacity loss rate of the suspected abnormally degraded battery is determined based on the second capacity loss rate. In addition, in response to the failure to identify the suspected abnormal degradation battery or the final capacity loss rate being lower than a first threshold, the state of charge range of all batteries is calculated based on the battery voltage data of the energy storage battery compartment in a static state, and the state of charge range is determined as the available capacity loss rate caused by abnormal battery consistency.

2. The method for identifying available capacity anomalies in an electrochemical energy storage system according to claim 1, characterized in that, The steps for preprocessing the runtime data include: Remove data points in the operating data whose battery voltage values ​​exceed the preset safe voltage range; The null values ​​in the running data are filled in; Determine whether the deep charge-discharge cycle meets the preset cycle conditions; the preset cycle conditions include: the maximum voltage at the charging end is greater than the first voltage threshold, the minimum voltage at the discharging end is less than the second voltage threshold, and the resting time after the discharge is completed is greater than the preset time.

3. The method for identifying anomalies in the usable capacity of an electrochemical energy storage system according to claim 2, characterized in that, After determining whether the deep charge-discharge cycle meets the preset cycle conditions, the method further includes: Calculate the total ampere-hour integral charge of the deep charge-discharge cycle; Determine whether the ratio of the total ampere-hour integrated capacity to the rated capacity of the energy storage battery compartment is within a preset reasonable range; If the ratio exceeds the preset reasonable range, the data for this loop is determined to be invalid and discarded.

4. The method for identifying available capacity anomalies in an electrochemical energy storage system according to claim 1, characterized in that, The steps for identifying whether there are any suspected abnormally degraded batteries in the energy storage battery compartment that meet a preset high-charge-low-discharge voltage deviation criterion include: For a battery cluster in the energy storage battery compartment, calculate the median of all battery voltages at the end of charging and the median of all battery voltages at the end of discharging. Calculate a first difference between the voltage of each battery at the end of charging and the median voltage at the end of charging, and a second difference between the voltage of each battery at the end of discharging and the median voltage at the end of discharging; Batteries that simultaneously satisfy the condition that the first difference is greater than the positive deviation threshold and the second difference is less than the negative deviation threshold are marked as suspected abnormal degradation batteries.

5. The method for identifying anomalies in the usable capacity of an electrochemical energy storage system according to claim 1, characterized in that, The formula for calculating the first capacity loss rate of the suspected abnormally degraded battery using the ampere-hour integration method is as follows: ； in, The first capacity loss rate; The actual charge / discharge amount is obtained by integrating the current over time during a deep charge / discharge cycle. This refers to the battery's nominal capacity.

6. The method for identifying anomalies in the usable capacity of an electrochemical energy storage system according to claim 1, characterized in that, The steps for calculating the second capacity loss rate of the suspected abnormally degraded battery based on the mapping relationship between open-circuit voltage and state of charge include: Obtain the interpolated open-circuit voltage-state-of-charge curve; Based on the voltage of the suspected abnormally degraded battery in a static state, its first state of charge value is determined using the open-circuit voltage-state-of-charge curve. Calculate the average state of charge of all normal batteries in the battery cluster, excluding the suspected abnormally degraded battery; The second capacity loss rate is calculated based on the first state of charge value and the average state of charge value.

7. The method for identifying anomalies in the usable capacity of an electrochemical energy storage system according to claim 1, characterized in that, The step of determining the final capacity loss rate of the suspected abnormally degraded battery based on the second capacity loss rate is as follows: calculate the arithmetic mean of the first capacity loss rate and the second capacity loss rate.

8. The method for identifying anomalies in the usable capacity of an electrochemical energy storage system according to claim 6, characterized in that, The steps for calculating the state-of-charge range of all batteries based on the battery voltage data of the energy storage battery compartment in a static state include: Select the battery voltage data at the moment when the deep charge-discharge cycle ends and the battery has been left to stand for a preset stable time. Using the open-circuit voltage-state-of-charge curve, the voltage of each battery is mapped to the corresponding state-of-charge value; Find the maximum and minimum values ​​of the state of charge (SOC) values ​​of all batteries, and calculate the difference between them as the SOC range.

9. The method for identifying anomalies in the usable capacity of an electrochemical energy storage system according to claim 1, characterized in that, After determining the final capacity loss rate or the available capacity loss rate, the method further includes: If the final capacity loss rate exceeds the second threshold, an operation and maintenance instruction is generated to recommend replacing the battery suspected of abnormal degradation. If the available capacity loss rate is lower than the third threshold, an operation and maintenance instruction is generated to enable the battery management system balancing function and continuously monitor it. If the available capacity loss rate exceeds the third threshold, an operation and maintenance instruction is generated to perform recharging or discharging operations on the battery with abnormal state of charge.

10. A capacity anomaly identification system for an electrochemical energy storage system, characterized in that, The system includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the method for identifying anomalies in the available capacity of an electrochemical energy storage system as described in any one of claims 1 to 9.

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